Research map / Detection and segmentation / Open-set and few-shot detection
Image Matting without Matting-Specific Annotations via Eikonal Fields
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes FreeMatte, an image matting approach that avoids alpha-matte and trimap supervision by using Eikonal fields, as described in the abstract.
Problem
Deep image matting relies on costly task-specific annotations (alpha mattes and trimaps), limiting use in annotation-scarce settings.
Approach
Annotation-free matting framework built on Eikonal fields to estimate continuous alpha mattes without matting-specific labels; specific mechanism not detailed in the abstract.
Closest papers by meaning
- Training Matting Models Without Alpha Labels (AAAI 2025 · similarity 0.92)
- Attention-Guided Hierarchical Structure Aggregation for Image Matting (CVPR 2020 · similarity 0.91)
- In-Context Matting (CVPR 2024 · similarity 0.90)
- DRIP: Unleashing Diffusion Priors for Joint Foreground and Alpha Prediction in Image Matting (NeurIPS 2024 · similarity 0.89)
- Privileged Prior Information Distillation for Image Matting (AAAI 2024 · similarity 0.89)
- Revisiting Context Aggregation for Image Matting (ICML 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
